Authors:
(1) Diwen Xue, University of Michigan;
(2) Reethika Ramesh, University of Michigan;
(3) Arham Jain, University of Michigan;
(4) Arham Jain, Merit Network, Inc.;
(5) J. Alex Halderman, University of Michigan;
(6) Jedidiah R. Crandall, Arizona State University/Breakpointing Bad;
(7) Roya Ensaf, University of Michigan. Table of Links Abstract and 1 Introduction 2 Background & Related Work 3 Challenges in Real-world VPN Detection 4 Adversary Model and Deployment 5 Ethics, Privacy, and Responsible Disclosure 6 Identifying Fingerprintable Features and 6.1 Opcode-based Fingerprinting 6.2 ACK-based Fingerprinting 6.3 Active Server Fingerprinting 6.4 Constructing Filters and Probers 7 Fine-tuning for Deployment and 7.1 ACK Fingerprint Thresholds 7.2 Choice of Observation Window N 7.3 Effects of Packet Loss 7.4 Server Churn for Asynchronous Probing 7.5 Probe UDP and Obfuscated OpenVPN Servers 8 Real-world Deployment Setup 9 Evaluation & Findings and 9.1 Results for control VPN flows 9.2 Results for all flows 10 Discussion and Mitigations 11 Conclusion 12 Acknowledgement and References Appendix 6.4 Constructing Filters and Probers Our Filter performs both opcode and ACK-based fingerprinting, flagging a flow if at least one fingerprint matches. This is because the opcode and ACK fingerprints are designed to be complementary: both are effective against vanilla OpenVPN and they each target a specific subset of obfuscations. The former works against XOR-based obfuscations that work like Vigenère ciphers, i.e. they always encrypt the same plaintext opcodes at the same position to the same ciphertext bytes. The latter targets tunneling-based obfuscation that lacks random padding and preserves the 1:1 correspondence between the original and obfuscated packet streams. Combining the two features maximizes our fingerprinting coverage, as we discovered that even within the same provider, obfuscating strategies can vary a lot (§ 9). Table 5 in Appendix shows the effectiveness of each feature against each commercial VPN service we tested. Following Filter’s result, the Prober performs the active probing scheme to further lower potential false positives. We implement the Filter in Zeek [75], an open-source network monitoring tool. We note that the evaluation processes for opcode and ACK-based fingerprinting are quite simple: both only require several dozen integer comparisons (limited by the observation window) while maintaining a small number of per-flow states. We implement the Prober in Nim [31]. We believe that both components can be easily deployed by any ISP or censor. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Diwen Xue, University of Michigan; (2) Reethika Ramesh, University of Michigan; (3) Arham Jain, University of Michigan; (4) Arham Jain, Merit Network, Inc.; (5) J. Alex Halderman, University of Michigan; (6) Jedidiah R. Crandall, Arizona State University/Breakpointing Bad; (7) Roya Ensaf, University of Michigan. Authors: Authors: (1) Diwen Xue, University of Michigan; (2) Reethika Ramesh, University of Michigan; (3) Arham Jain, University of Michigan; (4) Arham Jain, Merit Network, Inc.; (5) J. Alex Halderman, University of Michigan; (6) Jedidiah R. Crandall, Arizona State University/Breakpointing Bad; (7) Roya Ensaf, University of Michigan. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Related Work 2 Background & Related Work 3 Challenges in Real-world VPN Detection 3 Challenges in Real-world VPN Detection 4 Adversary Model and Deployment 4 Adversary Model and Deployment 5 Ethics, Privacy, and Responsible Disclosure 5 Ethics, Privacy, and Responsible Disclosure 6 Identifying Fingerprintable Features and 6.1 Opcode-based Fingerprinting 6 Identifying Fingerprintable Features and 6.1 Opcode-based Fingerprinting 6.2 ACK-based Fingerprinting 6.2 ACK-based Fingerprinting 6.3 Active Server Fingerprinting 6.3 Active Server Fingerprinting 6.4 Constructing Filters and Probers 6.4 Constructing Filters and Probers 7 Fine-tuning for Deployment and 7.1 ACK Fingerprint Thresholds 7 Fine-tuning for Deployment and 7.1 ACK Fingerprint Thresholds 7.2 Choice of Observation Window N 7.2 Choice of Observation Window N 7.3 Effects of Packet Loss 7.3 Effects of Packet Loss 7.4 Server Churn for Asynchronous Probing 7.4 Server Churn for Asynchronous Probing 7.5 Probe UDP and Obfuscated OpenVPN Servers 7.5 Probe UDP and Obfuscated OpenVPN Servers 8 Real-world Deployment Setup 8 Real-world Deployment Setup 9 Evaluation & Findings and 9.1 Results for control VPN flows 9 Evaluation & Findings and 9.1 Results for control VPN flows 9.2 Results for all flows 9.2 Results for all flows 10 Discussion and Mitigations 10 Discussion and Mitigations 11 Conclusion 11 Conclusion 12 Acknowledgement and References 12 Acknowledgement and References Appendix Appendix 6.4 Constructing Filters and Probers Filters Our Filter performs both opcode and ACK-based fingerprinting, flagging a flow if at least one fingerprint matches. This is because the opcode and ACK fingerprints are designed to be complementary: both are effective against vanilla OpenVPN and they each target a specific subset of obfuscations. The former works against XOR-based obfuscations that work like Vigenère ciphers, i.e. they always encrypt the same plaintext opcodes at the same position to the same ciphertext bytes. The latter targets tunneling-based obfuscation that lacks random padding and preserves the 1:1 correspondence between the original and obfuscated packet streams. Combining the two features maximizes our fingerprinting coverage, as we discovered that even within the same provider, obfuscating strategies can vary a lot (§ 9). Table 5 in Appendix shows the effectiveness of each feature against each commercial VPN service we tested. Following Filter’s result, the Prober performs the active probing scheme to further lower potential false positives. Filter Filter’s Prober We implement the Filter in Zeek [75], an open-source network monitoring tool. We note that the evaluation processes for opcode and ACK-based fingerprinting are quite simple: both only require several dozen integer comparisons (limited by the observation window) while maintaining a small number of per-flow states. We implement the Prober in Nim [31]. We believe that both components can be easily deployed by any ISP or censor. Filter Prober This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Fine-Tuning OpenVPN Detection: ACK Thresholds and Performance Metrics

Active Probing: How OpenVPN Servers Are Fingerprinted Through Unique Behaviors

VirtualMachine.Tech

Too Long; Didn't Read

CTA: Download the FREE AI Productivity Shift report

Enhancing OpenVPN Detection: Combining Fingerprinting and Active Probing

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

Active Probing: How OpenVPN Servers Are Fingerprinted Through Unique Behaviors

Unveiling OpenVPN: Fingerprinting with ACK Patterns and Timing Analysis

Techniques for Detecting Discrimination in Streaming Services

How Real-World Censors Detect OpenVPN Traffic: A Two-Phase Approach

OpenVPN is Open to VPN Fingerprinting: Real-world Deployment Setup

Active Probing: How OpenVPN Servers Are Fingerprinted Through Unique Behaviors

Unveiling OpenVPN: Fingerprinting with ACK Patterns and Timing Analysis

Techniques for Detecting Discrimination in Streaming Services

How Real-World Censors Detect OpenVPN Traffic: A Two-Phase Approach

OpenVPN is Open to VPN Fingerprinting: Real-world Deployment Setup

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps